The field of Artificial Intelligence (AI) has existed for over fifty years. Many useful programs have been created from artificial intelligence research such as expert systems, skilled game playing programs, and neural network based pattern matching systems. Many of the programs can accomplish feats that no human could possibly match due to the significant computational power of modem computer systems. However, no computer program has ever shown the type of understanding exhibited by the brain of even a young child.
There have been two main schools of artificial intelligence research: classic artificial intelligence research and neural network research. These two main schools of artificial intelligence research differ in how the problem of attempting to create machine intelligence is addressed. The main difference between the two schools is in how the two schools of artificial intelligence research are related to known information about the human brain.
Classic artificial intelligence proponents make no attempt to examine or replicate the manner in which the human brain operates. Proponents of classic artificial intelligence attempt to create programs that mimic basic human behaviors or problem solving in a manner that does not incorporate any fundamental understanding of how human brains actually work. People that followed the classic artificial intelligence research school of thought felt that they should not be limited by the particular solution discovered by nature. This school of thought has some resonance since we created flying machines that do not operate the way birds fly and we created fast land travel machines that do not operate the way a cheetah runs.
To create classic artificial intelligence, a programmer examines the problem to be solved or the human behavior to be mimicked and then determines an algorithmic solution to the problem. The programmer then codes the algorithmic solution in computer software. Examples of classic artificial intelligence programs include chess playing programs and expert system programs. These programs use an algorithm solution and a set of rules created by a human expert in order to solve complex problems, respectively. However, these programs generally have no ability to learn. These programs can only handle the single problem that was addressed. Nor can such artificial intelligence programs generalize upon the knowledge incorporated into such programs in order to address completely novel input data.
Neural network proponents have attempted to create limited intelligent systems by replicating the operation of interconnected neural cells. There is a large body of knowledge that describes how individual neural cells (neurons) operate and how connected neurons interact. Neural network proponents have built systems, known as “neural networks”, based upon this knowledge about neuron operation. Thus, neural network systems operate in a manner similar to a set of interconnected neurons. Neural network researchers are therefore often referred to as ‘connectionists.’ Interneuron connection strengths are known as synaptic weights and are used to store the learned knowledge.
Before being used, a neural network must first be trained with a set of training information. The training information consists of input vectors with associated output vectors that are deemed to be the correct output for the associated input vector. During the training, the connections between the various simulated neurons in the neural network are adjusted such that the input vectors generate the associated output vectors (or a close approximation).
Once trained, a neural network is used by presenting a novel input vector to the neural network such that an output vector is generated. With a proper neural network design and adequate training data, the neural network should generate the appropriate output vector for the given input vector. Neural networks have been proven to be useful in some limited applications.
Although there have been some limited successes with neural networks, most neural network systems are relatively primitive. Most neural network systems are simply a three layer structure with a set of input nodes, a set of middle nodes (also known as the ‘hidden nodes’), and a set of output nodes. Although neural network systems are able to ‘learn’ in a very simple sense and exhibit a limited ability to generalize, there is clearly no real understanding of the world. Neural network systems merely create an internal function that best maps the training input vectors to the associated training output vectors. Thus, a neural network is only able to generalize in a limited sense by applying the internal function to the novel input vectors.
To really advance the field of artificial intelligence, a new paradigm for artificial intelligence would be desirable. The classic artificial intelligence approach has probably failed since we do not fully understand the essence of intelligence. And without understanding the essence of intelligence, how can one be expected to encode intelligence in a computer program? The neural network approach has provided very limited results since neural networks generally emulate only relatively few interconnected neurons and does so in a manner that ignores most of the complex anatomy of the brain. Since current estimates postulate that the neocortex of the human brain contains approximately thirty billion neurons, such simple neural networks will never provide the real intelligence exhibited by the human brain. Thus, to advance the state of artificial intelligence it would be desirable to embark on a new approach that avoids the problems of the current main approaches.